Papers
8,340 papers found
Neural Diffusion Processes
Vincent Dutordoir, Alan Saul, Zoubin Ghahramani et al.
Neural FIM for learning Fisher information metrics from point cloud data
Oluwadamilola Fasina, Guillaume Huguet, Alexander Tong et al.
Neural Inverse Operators for Solving PDE Inverse Problems
Roberto Molinaro, Yunan Yang, Björn Engquist et al.
Neural Latent Aligner: Cross-trial Alignment for Learning Representations of Complex, Naturalistic Neural Data
Cheol Jun Cho, Edward Chang, Gopala Anumanchipalli
Neural Markov Jump Processes
Patrick Seifner, Ramses J Sanchez
Neural Network Accelerated Implicit Filtering: Integrating Neural Network Surrogates With Provably Convergent Derivative Free Optimization Methods
Brian Irwin, Eldad Haber, Raviv Gal et al.
Neural Network Approximations of PDEs Beyond Linearity: A Representational Perspective
Tanya Marwah, Zachary Chase Lipton, Jianfeng Lu et al.
Neural networks trained with SGD learn distributions of increasing complexity
Maria Refinetti, Alessandro Ingrosso, Sebastian Goldt
Neural Prediction Errors enable Analogical Visual Reasoning in Human Standard Intelligence Tests
Lingxiao Yang, Hongzhi You, Zonglei Zhen et al.
Neural signature kernels as infinite-width-depth-limits of controlled ResNets
Nicola Muca Cirone, Maud Lemercier, Cristopher Salvi
NeuralSlice: Neural 3D Triangle Mesh Reconstruction via Slicing 4D Tetrahedral Meshes
Chenbo Jiang, Jie Yang, Shwai He et al.
NeuralStagger: Accelerating Physics-constrained Neural PDE Solver with Spatial-temporal Decomposition
Xinquan Huang, Wenlei Shi, Qi Meng et al.
Neural Status Registers
Lukas Faber, Roger Wattenhofer
Neural Stochastic Differential Games for Time-series Analysis
Sungwoo Park, Byoungwoo Park, Moontae Lee et al.
Neural Wasserstein Gradient Flows for Discrepancies with Riesz Kernels
Fabian Altekrüger, Johannes Hertrich, Gabriele Steidl
Neuro-Symbolic Continual Learning: Knowledge, Reasoning Shortcuts and Concept Rehearsal
Emanuele Marconato, Gianpaolo Bontempo, Elisa Ficarra et al.
Never mind the metrics---what about the uncertainty? Visualising binary confusion matrix metric distributions to put performance in perspective
David Lovell, Dimity Miller, Jaiden Capra et al.
New metrics and search algorithms for weighted causal DAGs
Davin Choo, Kirankumar Shiragur
NNSplitter: An Active Defense Solution for DNN Model via Automated Weight Obfuscation
Tong Zhou, Yukui Luo, Shaolei Ren et al.
Node Embedding from Neural Hamiltonian Orbits in Graph Neural Networks
Qiyu Kang, Kai Zhao, Yang Song et al.
Non-autoregressive Conditional Diffusion Models for Time Series Prediction
Lifeng Shen, James Kwok
Nonlinear Advantage: Trained Networks Might Not Be As Complex as You Think
Christian H.X. Ali Mehmeti-Göpel, Jan Disselhoff
Nonlinear Causal Discovery with Latent Confounders
David Kaltenpoth, Jilles Vreeken
Nonparametric Density Estimation under Distribution Drift
Alessio Mazzetto, Eli Upfal